This disclosure relates generally to the field of face recognition. More particularly, this disclosure describes a number of techniques for combining multiple types of face recognition descriptors into a single entity—a face feature vector. Face feature vectors may be used in face recognition applications. Examples of such applications include, but are not limited to, managing, sorting and annotating images (still and video) in iPhoto® and Aperture®. (IPHOTO and APERTURE are registered trademarks of Apple Inc.)
In general terms, face recognition operations scan a person's face, extract or detect a specified set of parameters therefrom, and match those parameters against a library of known facial data to which identification has been previously assigned or is otherwise known. The data set to which a new image's parameters are compared is often times characterized or described by a model. In practice, these models define groups of parameter sets where all images falling within a given group are classified as belonging to the same person.
To be robust (e.g., stable to image noise, a person's pose, and scene illumination) and accurate (e.g., provide high recognition rates) the specified parameter sets need to encode information that describes a face in a way that is repeatable and invariant to typical intra-person variability while at the same time being able to discriminate a one person from another. This need is a central problem encountered by all face recognition systems. Thus, it would be beneficial to identify a mechanism (methods, devices, and systems) to define a set of parameters that provide robust and accurate face recognition.